DNA-Driven EEG monitoring for rapid seizure prediction in healthcare

Worldwide, over 50 million people suffer from epilepsy, a neurological disorder characterised by recurrent seizures due to abnormal electrical activity in the brain. These occur as a result of sudden electric surges and the symptoms vary based on the region of the brain being affected, including brief staring spells and confusion to convulsions and loss of consciousness. Physicians typically classify seizures into four main phases: Interictal, Preictal, Ictal, and Postictal. Accurate analysis of EEG signals around seizure onset is extremely critical for timely clinical intervention. However, the current methodologies majorly utilise complex Convolutional Neural Networks (CNNs) with millions of parameters. They require high computational power, and, hence, it is difficult to deploy them in wearable devices. The core idea of this work is to develop a computationally compact architecture for seizure onset discrimination that offers potential for future integration with wearable devices.

To achieve this, this work proposes employing a DNA-based encoding framework for Electroencephalogram (EEG) signals. Existing DNA-based compression techniques have demonstrated significant potential in reducing data complexity. Multichannel EEG signals using 23 scalp electrodes are obtained from the CHB-MIT dataset and normalised using min-max scaling. The signals are then windowed to capture temporal dependencies and transformed into integer safe magnitudes before being converted to binary. This approach then involves genetic coding-based preprocessing: genetic transcription and translation (DNA → RNA → Codons → Amino Acids) occur. By converting EEG signal data to amino acid sequences, the proposed encoding scheme aims to capture underlying patterns in the data and provide a compact representation of temporal patterns. The encoded sequences are subsequently processed using a lightweight one-dimensional multi-level parallel CNN architecture.

These DNA-encoded EEG sequences are then used as input to the proposed 1D multi-level parallel CNN model, with drastically fewer parameters. After extensive testing, the proposed model achieves an accuracy of 96.22%. Additionally, the applicability of the proposed encoding framework on early seizure prediction tasks under a subject-wise protocol has been evaluated. An accuracy of 93.87% has been achieved. Overall, these findings indicate that the proposed approach provides a compact and effective representation for EEG-based seizure analysis across related onset and early prediction tasks.